Most of the above methods define the forgery detection task as a general two classification problem, so the performance is sensitive to the quality of the data set or the data distribution. Since the differences between real and fake images are mostly subtle and local, many researchers currently define the problem of forgery detection as a fine-grained classification problem. Du  [1]} focus on local spatial features and learn the internal representation of local forged regions through auto-encoders to bridge the generalization differences. Zhao  [2]} propose a multi-attentional network to focus on different local parts and subtle artifacts. Wang  [3]} propose an attention-based data enhancement framework, which encourages the network to mine more diverse features by occluding sensitive areas. Wang  [4]} propose a multi-modal multi-scale transformer to capture the subtle manipulation artifacts at different scales. In this paper, we also treat the forgery detection task as a fine-grained classification problem. We focus on the local features and the establishment of associations among features.
